机器学习推荐书目 发表于 2013 年 8 月 22 日 由 justin 来源：水木社区人工智能版。 发信人: Insomnia (完美主义是种病), 信区: AI 标 题: Machine Learning书单 发信站: 水木社区 (Fri Mar 29 16:46:37 2013), 站内 持续更新，请补充。 除了以下推荐的书以外，出版在Foundations and Trends in Machine Learning上面的survey文章都值得一看。 入门： Pattern Recognition And Machine Learning Christopher M. Bishop Machine Learning : A Probabilistic Perspective Kevin P. Murphy The Elements of Statistical Learning : Data Mining, Inference, and Predictio n Trevor Hastie, Robert Tibshirani, Jerome Friedman Information Theory, Inference and Learning Algorithms David J. C. MacKay All of Statistics : A Concise Course in Statistical Inference Larry Wasserman 优化： Convex Optimization Stephen Boyd, Lieven Vandenberghe Numerical Optimization Jorge Nocedal, Stephen Wright Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Wright 核方法： Kernel Methods for Pattern Analysis John Shawe-Taylor, Nello Cristianini Learning with Kernels : Support Vector Machines, Regularization, Optimizatio n, and Beyond Bernhard Schlkopf, Alexander J. Smola 半监督： Semi-Supervised Learning Olivier Chapelle 高斯过程： Gaussian Processes for Machine Learning (Adaptive Computation and Machine Le arning) Carl Edward Rasmussen, Christopher K. I. Williams 概率图模型： Graphical Models, Exponential Families, and Variational Inference Martin J Wainwright, Michael I Jordan Boosting: Boosting : Foundations and Algorithms Schapire, Robert E.; Freund, Yoav 贝叶斯: Statistical Decision Theory and Bayesian Analysis James O. Berger The Bayesian Choice : From Decision-Theoretic Foundations to Computational I mplementation Christian P. Robert Bayesian Nonparametrics Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker Principles of Uncertainty Joseph B. Kadane Decision Theory : Principles and Approaches Giovanni Parmigiani, Lurdes Inoue 蒙特卡洛： Monte Carlo Strategies in Scientific Computing Jun S. Liu Monte Carlo Statistical Methods Christian P.Robert, George Casella 信息几何： Methods of Information Geometry Shun-Ichi Amari, Hiroshi Nagaoka Algebraic Geometry and Statistical Learning Theory Watanabe, Sumio Differential Geometry and Statistics M.K. Murray, J.W. Rice 渐进收敛： Asymptotic Statistics A. W. van der Vaart Empirical Processes in M-estimation Geer, Sara A. van de 不推荐： Statistical Learning Theory Vladimir N. Vapnik Bayesian Data Analysis, Second Edition Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin Probabilistic Graphical Models : Principles and Techniques Daphne Koller, Nir Friedman
Quantum physics is admittedly the most difficult subject to understand. Physicists, let alone students and laymen, are still puzzled by it today. As Richard Feynman once famously claimed, nobody understood quantum mechanics. The crux of the matter lies in the meaning of the mysterious wave function in the theory. An electron is represented by a wave function. But it remains unclear what physical state the mathematical wave function represents. Exactly what is an electron? Is it a spreading wave or a localized particle? If the electron is still a particle, then how does it move? e.g. how does a single electron pass through two slits? Understanding Quantum Mechanics by Shan Gao.pdf This book will show that the real meaning of the wave function can already be unveiled based on the established parts of quantum mechanics. It turns out that electrons are still localized particles, but their motion is not continuous but discontinuous and random, displaying wave-like behavior. For example, in the double-slit experiment with single electrons, each electron passes through two slits at the same time in a random and discontinuous way. Moreover, the book also answers another three fundamental questions about quantum mechanics. How come the Schrdinger equation? Does the wave function collapse during a measurement? If yes, then why and how? Is quantum mechanics compatible with special relativity? If not, then how to solve the incompatibility problem? The original ideas of this book, if confirmed, may finally unveil the mysterious quantum world and make quantum physics comprehensible for everyone. Book Thoughts Reviews This is an ambitious work that reflects admirable grip, and distinctive take, on much of the contemporary philosophy of quantum mechanics literature. ---- Reviewer of Philosophy of Science The idea of using discontinuous motion as a realist interpretation of quantum mechanics is original. ---- Reviewer of Foundations of Physics If it goes through, this would be an original and significant contribution to the debate over the nature of motion. ---- Reviewer of American Philosophical Quarterly Its very existence is at any rate, an excellent illustration of the extent to which physical data force us to depart from commonsense ideas when we try to depict reality “as it really is”. ---- Bernard d'Espagnat, Templeton Prize 2009 Laureate, author of Conceptual Foundations of Quantum Mechanics and On Physics and Philosophy 本书 电子版 （ ebook ）由 Amazon 于去年 11 月出版， 中英文纸印本将会扩充一些内容，计划于明年出版。 欢迎大家对书稿多提意见和建议。
1. Geoffrey Grimmett. Percolation (2. ed). Springer Verlag, 1999. 2. Harry Kesten , What is percolation? Notices of the AMS , May 2006. 3. Muhammad Sahimi. Applications of Percolation Theory . Taylor Francis, 1994. ISBN 0-7484-0075-3 (cloth), ISBN 0-7484-0076-1 (paper) 4. Percolation Theory for Flow in Porous Media (Lecture Notes in Physics) Series: Lecture Notes in Physics , Vol.771 Hunt , Allen, Ewing , Robert , 2nd ed., 2009, Approx. 310 p., Hardcover ISBN: 978-3-540-89789-7 5.Liggett, T. M. Lecture Notes in Particle Systems and Percolation . 6. Bela Bollobs , Oliver Riordan Percolation (to be continued)
标签: 参考书
